Artificial neural networks are computing systems with an architecture based on biological neural networks. Artificial neural networks can be trained, using training data, to learn about how to perform a certain computing task.
A neural network may include a set of processing nodes. Each processing node can process a piece of the input data based on a weight to generate an output. The outputs can be processed using an activation function to generate a decision. A neural network may be implemented by a neural network processor including, for example, circuitries and data paths, part of which can be used to implement the activation functions. The throughput and accuracy of the neural network processing may depend on how the activation functions are implemented in the neural network processor or other hardware components used for the neural network processing.